Case-based reasoning in IVF: prediction and knowledge mining.
In vitro fertilization (IVF) is a medically-assisted reproduction technique, enabling infertile couples to achieve successful pregnancy. Given the unpredictability of the task, we propose to use a case-based reasoning system that exploits past experiences to suggest possible modifications to an IVF treatment plan in order to improve overall success rates. Once the system's knowledge base is populated with a sufficient number of past cases, it can be used to explore and discover interesting relationships among data, thereby achieving a form of knowledge mining. The article describes the TA3IVF system--a case-based reasoning system which relies on context-based relevance assessment to assist in knowledge visualization, interactive data exploration and discovery in this domain. The system can be used as an advisor to the physician during clinical work and during research to help determine what knowledge sources are relevant for a treatment plan.
Jurisica
I
I
Department of Computer Science, University of Toronto, Ontario, Canada. juris@cs.utoronto.ca
Mylopoulos
J
J
Glasgow
J
J
Shapiro
H
H
Casper
R F
RF
D001185:Artificial Intelligence; D003627:Data Interpretation, Statistical; D005069:Evaluation Studies as Topic; D005260:Female; D005307:Fertilization in Vitro; D006801:Humans; D016347:Medical Records Systems, Computerized; D011237:Predictive Value of Tests; D011247:Pregnancy